Improving Computation of Sensible Heat Flux over a Water Surface Using the Variational Method
Bibliographic record
Abstract
Abstract In this study, the authors have performed the variational computations for surface sensible heat fluxes over a large northern lake using observed wind, temperature gradient, and moisture gradient. In contrast with the conventional (Monin–Obukhov similarity theory) MOST-based flux-gradient method, the variational approach sufficiently utilizes observational meteorological conditions over the lake, where the conventional flux-gradient method performs poorly. Verifications using direct eddy-correlation measurements over Great Slave Lake, the fifth largest lake in North America in terms of surface area, during the open water period of 1999 demonstrate that the variational method yields good agreements between the computed and the measured sensible heat fluxes. It is also demonstrated that the variational method is more accurate than the flux-gradient method in computations of sensible heat flux across the air–water interface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".